A new approach to improve accuracy of grey model GMC (1,n) in time series prediction
© 2015 Sompop Moonchai and Wanwisa Rakpuang. This paper presents a modified grey model GMC(1,n) for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n) model in order to improve its prediction accuracy by...
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th-cmuir.6653943832-446702018-04-25T07:53:24Z A new approach to improve accuracy of grey model GMC (1,n) in time series prediction Sompop Moonchai Wanwisa Rakpuang Agricultural and Biological Sciences © 2015 Sompop Moonchai and Wanwisa Rakpuang. This paper presents a modified grey model GMC(1,n) for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n) model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n) model was verified by two cases: the study of forecasting CO 2 emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n) model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n) and D-GMC(1,n) models. 2018-01-24T04:46:25Z 2018-01-24T04:46:25Z 2015-01-01 Journal 16875605 16875591 2-s2.0-84953214867 10.1155/2015/126738 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44670 |
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Agricultural and Biological Sciences Sompop Moonchai Wanwisa Rakpuang A new approach to improve accuracy of grey model GMC (1,n) in time series prediction |
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© 2015 Sompop Moonchai and Wanwisa Rakpuang. This paper presents a modified grey model GMC(1,n) for use in systems that involve one dependent system behavior and n-1 relative factors. The proposed model was developed from the conventional GMC(1,n) model in order to improve its prediction accuracy by modifying the formula for calculating the background value, the system of parameter estimation, and the model prediction equation. The modified GMC(1,n) model was verified by two cases: the study of forecasting CO 2 emission in Thailand and forecasting electricity consumption in Thailand. The results demonstrated that the modified GMC(1,n) model was able to achieve higher fitting and prediction accuracy compared with the conventional GMC(1,n) and D-GMC(1,n) models. |
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Sompop Moonchai Wanwisa Rakpuang |
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Sompop Moonchai Wanwisa Rakpuang |
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Sompop Moonchai |
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A new approach to improve accuracy of grey model GMC (1,n) in time series prediction |
title_short |
A new approach to improve accuracy of grey model GMC (1,n) in time series prediction |
title_full |
A new approach to improve accuracy of grey model GMC (1,n) in time series prediction |
title_fullStr |
A new approach to improve accuracy of grey model GMC (1,n) in time series prediction |
title_full_unstemmed |
A new approach to improve accuracy of grey model GMC (1,n) in time series prediction |
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new approach to improve accuracy of grey model gmc (1,n) in time series prediction |
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2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84953214867&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44670 |
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